As AI tools become increasingly embedded in course design workflows, the question isn't just whether to use them, it's how to use them responsibly and ethically. This session explores the UCF RN-BSN program's partnership with iDesign to redesign 17 fully online, accelerated and compressed nursing courses. Attendees will learn how an "AI-informed, AI-supported" framework keeps human experts in control of content decisions while AI handles alignment checks, workload calibration, and quality assurance. Ethical considerations are built into the workflow, with humans maintaining authority over academic and content choices at every stage. The session offers a transferable model for institutions navigating AI adoption without sacrificing academic oversight.
Whitney is the Chief Academic Officer at iDesign working with institutions of higher education to build high quality online and blended learning programs. Her primary areas of focus are faculty professional development, personalized adaptive digital content, and learner engagement... Read More →
Assistant Program Director, University of Central Florida
Amanda Major, EdD, PMP, ACP, CPTD has experience delivering results in higher education digital learning. She brings to her role as an assistant program director for the University of Central Florida's Pegasus Innovation Lab experience as a higher education faculty, staff, and administrator... Read More →
As AI becomes more embedded in workplaces, students face growing uncertainty about their job search, job security, and shifting role expectations. Headlines often amplify fears of AI replacing workers, yet the reality is more nuanced: students must build in-demand skills, navigate psychological safety, and prepare for ongoing reskilling. This session examines how AI shapes students’ future employment experiences through role identity, role conflict, and role ambivalence. Participants will explore how these dynamics influence student attitudes and how higher education leaders can reduce fear, strengthen adaptability, and empower students for a transforming workforce.Keywords: AI Literacy in Higher Education, Student Career Development, Future‑Ready Skills
How do we cultivate AI literacy across the full spectrum of faculty readiness—from apprehensive to advancing? This session unveils a human-centered, data-informed AI strategy developed at Fort Lewis College, a rural NASNTI institution. Grounded in faculty survey data and responsive to the unique needs of Native American-serving contexts, our approach employs two complementary frameworks—an AI Course Design Framework and an AI Faculty Engagement Framework—to create scalable, values-driven pathways for pedagogical transformation. Participants will explore how equity-centered strategy design, targeted faculty development, and culturally responsive AI integration can spark meaningful institutional change.Keywords: #facultydevelopment #humancenteredAI #equitableintegration
Incorporating AI into career readiness for college students can help them build the essential skills needed to thrive in a rapidly evolving workforce. AI tools can support students in areas such as résumé building, interview preparation, and professional communication by offering personalized feedback and real time suggestions. In the classroom, AI can simulate the job search process, giving students hands-on experience with tools commonly used by recruiters to evaluate job applications. By integrating AI literacy into career readiness programming, colleges can ensure students are not just job ready, but future ready.
Career Development Training Specialist, University of Central Florida
Experienced higher education professional with over 15 years in leadership and program management roles. Recognized for excellence in supervision, training, and developing innovative career readiness initiatives for diverse student populations.
This session explores how an integrated AI Studio serves as a high-velocity engine to drive the prototyping of sophisticated digital tools, allowing academic leaders to move from conceptual “vibes” to functional architectures in record time. While AI accelerates early generation, the session emphasizes that Vibe Coding still requires technical rigor and a full-stack mindset. Attendees will see how natural language intent, rapid iteration, and disciplined design can advance curriculum innovation, competency mapping, assessment generation, Competency-Based Education, and Prior Learning Assessment, shortening the path from strategic idea to digital reality.
Ready to put AI to work in your online courses, without the guesswork? This session covers practical strategies for integrating generative AI across four key areas: developing AI policies that actually work, designing assessments that account for AI use, creating engaging online discussions that promote critical thinking, and streamlining the creation of instructional materials. You'll see real examples from two different courses and walk away with concrete techniques you can implement immediately. Whether you're just AI-curious or ready to transform your course design, no tech expertise required. Just bring your questions!
As generative AI (GenAI) tools move from experimentation to everyday use, higher education lacks practical methods for testing whether prompts perform reliably across consistent examples. This session presents a replicable prompt evaluation process using controlled multi-case testing, human rubric-based review, and revision cycles. Participants will examine ways to evaluate a GenAI prompt across varied scenarios, identify failure indicators, and iteratively refine performance by prioritizing human expertise. Together, these strategies build a practical model for evaluating GenAI integration into instructional and institutional workflows. #AI-evaluation #human-in-the-loop #prompt-fidelity
Director, Digital Learning Innovation, University of Central Florida
As program director of UCF’s iLab, Dr. Howard’s primary focus is to strategically align, promote, and provide project management support for initiatives that contribute to the lab’s mission to serve as an incubator for the next generation of digital learning by supporting faculty... Read More →
Large language models are able to identify pedagogical intent in course packages, such as Common Cartridge files, and use that intent to generate structured data that supports systematic course revision and redesign. This session demonstrates an AI-assisted workflow to import existing courses, analyze content, determine instructional function, and transform materials into new templates and adjusted session lengths before exporting revised courses back into a learning management system. By focusing on what instructional elements are designed to accomplish, this approach enables scalable redesign, visual standardization, and enhancements that support Universal Design for Learning, while preserving human instructional judgment.#AI-assisted-workflows #Course-revision-and-redesign #Instructional-design
As educators adopt AI tools, consistency and reliability are as important as innovation. This presentation focuses on how AI can be used to produce repeatable, dependable results across core instructional tasks, including content creation, student communication, feedback, grading support, and learning analytics. Through real classroom workflows, participants will see how structured prompts, clear guardrails, and intentional human oversight allow AI to enhance efficiency while preserving pedagogical quality. Attendees will leave with strategies for making AI use predictable, transparent, and sustainable across courses and terms.
I am a computer Science professor who has been working with the LLMs that have come out since 2021 to leverage them as tools for education. I have moved to the next level to offer these tools to other educators through our platform at gradassist.ai
Friday June 12, 2026 3:40pm - 4:10pm EDT Lafayette 3
As generative AI becomes embedded in higher education, many instructors struggle to prevent AI tools from replacing student learning. This session introduces a practical, instructor-controlled approach to designing custom AI teaching tools that prioritize guidance, feedback, and explanation instead of content generation. Participants will learn a step-by-step framework for building task-specific custom AI tools and explore adaptable examples that can be implemented across disciplines and course formats. Attendees will leave with concrete strategies they can apply immediately in their own teaching. (custom AI tools, instructor-designed AI tools, AI pedagogy)
